Intel/ColBERT-NQ

ColBERT NQ Checkpoint

The ColBERT NQ Checkpoint is a trained model based on the ColBERT architecture, which itself leverages a BERT encoder for its operations. This model has been specifically trained on the Natural Questions (NQ) dataset, focusing on text retrieval tasks.

Model Detail	Description
Model Authors	?
Date	Feb 7, 2023
Version	Checkpoint
Type	Text retrieval
Paper or Other Resources	Base Mode: ColBERT Dataset: Natural Questions
License	Other
Questions or Comments	Community Tab and Intel DevHub Discord

Intended Use	Description
Primary intended uses	This model is designed for text retrieval tasks, allowing users to submit queries and receive relevant passages from a corpus, in this case, Wikipedia. It can be integrated into applications requiring efficient and accurate retrieval of information based on user queries.
Primary intended users	Researchers, developers, and organizations looking for a powerful text retrieval solution that can be integrated into their systems or workflows, especially those requiring retrieval from large, diverse corpora like Wikipedia.
Out-of-scope uses	The model is not intended for tasks beyond text retrieval, such as text generation, sentiment analysis, or other forms of natural language processing not related to retrieving relevant text passages.

Evaluation

The ColBERT NQ Checkpoint model has been evaluated on the NQ dev dataset with the following results, showcasing its effectiveness in retrieving relevant passages across varying numbers of retrieved documents:

NQ	Recall	MRR
10	71.1	52.0
20	76.3	52.3
50	80.4	52.5
100	82.7	52.5

These metrics demonstrate the model's ability to accurately retrieve relevant information from a corpus, with both recall and mean reciprocal rank (MRR) improving as more passages are considered.

Ethical Considerations

While not specifically mentioned, ethical considerations for using the ColBERT NQ Checkpoint model should include awareness of potential biases present in the training corpus (Wikipedia), and the implications of those biases on retrieved results. Users should also consider the privacy and data use implications when deploying this model in applications.

Caveats and Recommendations

• Index Creation: Users need to build a vector index from their corpus using the ColBERT codebase before running queries. This process requires computational resources and expertise in setting up and managing search indices.
• Data Bias and Fairness: Given the Wikipedia-based training corpus, users should be mindful of potential biases and the representation of information within Wikipedia, adjusting their use case or implementation as necessary to address these concerns.